Preprocessing techniques for context recognition from accelerometer data
Preprocessing techniques for context recognition from accelerometer data
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DOI：
10.1007/s00779-010-0293-9
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发表时间：
2010-10-01
影响因子：
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通讯作者：
Cardoso, Joao M. P.
中科院分区：
文献类型：
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作者：
Figo, Davide;Diniz, Pedro C.;Cardoso, Joao M. P.
诸如智能电话之类的通信设备的普及已经导致能够响应特定用户活动或上下文的上下文感知服务的出现。这些服务使通信提供商能够为社交网络、老年人护理和准紧急预警系统等广泛应用开发新的增值服务。这些服务的核心是能够使用内部或外部传感器检测特定的物理设置或用户所处的环境。例如，使用内置的加速度计，可以确定用户在一天中的特定时间是步行还是跑步。通过将这些知识与GPS数据相关联，可以向具有类似日常生活的用户提供特定的信息服务。本文介绍了一个调查的技术，从原始加速度计数据提取此活动信息。可以在移动的设备中实现的技术的范围从诸如FFT的经典信号处理技术到当代的基于字符串的方法。我们目前的实验结果比较和评估的准确性，使用从日常活动收集的真实的数据集的各种技术。
The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.